abhisuri97

I think it’s a bug because a) 100%+ cpu usage is ridiculous even for normal spotlight searches past the initial spotlight indexing operation and b) this definitely worked without an issue or insane cpu usage before Catalina (checked on old MBP on Sierra).

The option to use spotlight as a calculator is part of its feature set. And the fact that it still does recognize (after a lot of cpu usage) that I wanted to make a calculation does indicate that the calculator functionality is still there. It just seems to be triggering some huge operation that requires even more cpu usage than a normal spotlight search.

I was an avid calculator spotlight user; however, ever since Catalina CPU spikes have been insanely high, I have stopped using it. And most of the other users who noticed this started posting about it this year, so I am sure that there was some change that was rolled back that caused spotlight to stop recognizing when a calculation was being invoked (but I could be wrong).

So what does that mean? ±.5°F? ±1°F? Does that vary at higher vs lower temperatures? What's the actual range it can handle? How does it work for people w/different skin colors (since this seems to be just based off of imaging data alone)? How about older phones w/lower resolution? How about cameras w/different fps?

Unsure if it's just me not being able to find stuff on their website, but is there any mention of how accurate this is? The page isn't really making me confident that it is achieving all the things they say it does...

It's insane that medical data has become so highly valued on the black market (I guess because of the amount of information stored with a single patient such as SSNs, maybe billing info, addresses, etc). Yet, it seems that the healthcare industry is not well prepared to deal with a huge amount of sensitive data (and doesn't seem to be obfuscating it when sharing with vendors). Ironically, I feel that the most secure way of doing patient record keeping is keeping everything physical.

a) Nice to see some Penn people here :)

b) I was wondering if you could give some documentation for what's in this repo. I'm looking around right now and I think I've found the main yaml config file (I'm guessing its the one in research/configs/default.yaml), but I'm unsure what else is included in the boilerplate (outside of the libraries you've mentioned) and how to use it.

Good writeup, but I'm unsure about the clinical use case here. Isn't it pretty rare for a doctor to first learn about a Covid diagnosis from a CT scan? Rather, I think a tool that can isolate the location of GGOs and monitor their progression across a few metrics (overall volume, density, etc) might be slightly more useful for a clinician as they consider disease progression. Of course, I guess that would mean that someone has to segment CT scans to mark where the GGOs are.

Honestly, I'm not sure why "master" became the name in the first place. To a beginner, it seems unintuitive (at least when I first learned git, I would not have thought "master" actually described what master was). I hope something common like "main" is adopted.

Wow. I really hope they’ll get it done before the expiration date but I always thought they’d be renewing months in advance at minimum. Are they trying to negotiate something?

https://www.reddit.com/u/dinosauce313 for reference.

Okay so...I'm actually in a rare position where I might be able to contribute to discussion on this! Brief caveats: am undergrad pre-med, but also took an infectious diseases grad class at Penn that spent a week diving into one of the papers cited in this article (when the authors mentioned the two compounds "showed no statistically significant difference in potency"). I think it's worth exploring what the authors of that paper actually meant to assess for. (also @HN please shoot me down if anything I say is inaccurate, also I do think the authors make other valid points, just not so much in reference to this particular aspect)

The paper in question is here (https://mbio.asm.org/content/9/2/e00221-18) and they're looking at table 1. First of all, the paper didn't set out to actually determine whether GS-441524 is better than GS-5734 (aka remdesivir). It mainly set out to show that remdesivir could work against coronaviruses in general (this was written back in 2018, so they were focusing on SARS and MERS only). Furthermore, all of these experiments were in vitro with a model betaCoV, murine hepatitis virus, so results shouldn't necessarily mean much for human populations. But down to the nitty-gritty:

In figure 1 of the paper, the authors do in vitro experiments to show that both GS-441524 and remdesivir reduce their model CoV viral titer. However, they show a major difference. Remdesivir has an EC50 (dose at which 50% of viral replication is inhibited) that is much lower than GS-441524. This is great because you don't need as much remdesivir to inhibit viral replication. One area where GS-441524 did beat remdesivir was cytotoxicity (CC50 = dose at which 50% of cells w/the drug is killed). You want as high a CC50 as possible. So that would mean that remdesivir is technically more toxic to cells; however, it's effective dose is so low that you wouldn't need to give a dose large enough to kill off any cells. You can use a handy metric known as the selective index (CC50/EC50) to measure the tradeoff. High selective index = high efficacy without killing off your own cells. In this case, remdesivir has a higher selective index compared to GS-441524.

Now onto what the article referred to. The authors made their in vitro studies slightly more relevant to humans by using human epithelial cells and this time actual SARS CoV and MERS CoV virus and measured EC50 and CC50 again for both GS-441524 and remdesivir. The authors report some +/- standard deviation ranges. From intro stat, I think one of the conclusions about looking at overlapping standard deviation bars is that no conclusion can be made about whether or not the differences are statistically significant or not! I encourage people to take a look at the table itself and make your own conclusions (https://mbio.asm.org/content/9/2/e00221-18#T1)

The rest of the paper focused on remdesivir and showed a number of things. It showed it was most effective early post infection (in vitro) and it highly inhibited viral replication when viral exonuclease was knocked out (indicating that remdesivir messes with the function of that). It also showed (via serial passage experiments...basically think about it as mini-artificial selection) that nucleoside analog drugs will tend to favor the selection for two RNA-dependent RNA polymerase mutant viruses that confer resistance to the nucleoside drugs! But they showed in the next figure that these mutant viruses have a reproductive disadvantage compared to their wild type CoV.

Now for my 2 cents: I think that remdesivir was favored primarily because it was being used for anti-viral treatment in Ebola virus trials...but it was falling out of favor for Ebola treatment because of other monoclonal antibody cocktail treatments that show better survival rates (ref PALM trial: https://www.nejm.org/doi/full/10.1056/NEJMoa1910993). Sooo I guess gilead had this drug lying around, and they had a good idea as to its mechanism of action...so why not apply it for Sars CoV2?

Ignorant American here who has never heard of Lidl/Schwarz Group before. Is there anything in their history that would show that they know how to do compute? Is their infrastructure right now really solid/better than AWS?

Well...at least they’re open sourcing this which is better than letting some company develop the app entirely and keep the code under lock and key.

Cue another “college is useful/useless” debate in the HN comments...

It's just about taking a bet. I'm sure that magic leap must have had some demo (perhaps rehearsed) of a rudimentary alpha version they showed investors that was enough to convince people it was possible. From an investor standpoint, there isn't much downside outside of losing money. But the potential upside would have been huge.

Disclaimer: didn't make it but found it an interesting tool to share.

I taught a course on Javascript in undergrad with a unit on react as well as redux (cis.upenn.edu/~cis197, check it out!). To this day, I still think people learning JS should know about redux because of the design pattern it encourages (note that when people are first learning CS, this is probably the first-ish time they're coming across the concept of a reducer and "functional" state that isn't mutated directly).

I think it's even more important for people self-learning react to be exposed to redux in some way; yeah, there's boilerplate, but it's just beautiful when all put together and isn't a Blackbox (I love dan's tutorial on egghead explaining redux from the ground up precisely because it breaks down exactly what's working and why it is).

This blog post does an excellent job of convincing newcomers in the JS world to consider learning redux. I'm definitely forwarding this to the current instructor of the course!

bio undergrad so take w/grain of salt 1. No, because serum does not contain any cells that have the markers of a bloodtype. You're just transferring the antibodies pretty much. When you spin down a blood sample in a centrifuge, the bottom pellet contains blood cells, the supernatant is plasma. From that supernatant, when you get rid of clotting factors, the remainder is just soluble molecules and antibodies. 2. Maybe. Serum can also contain viruses, but I assume the serum will be treated with chemicals to "clean it up" and just isolate antibodies.

I think that if a blog shilling a company/product is hosted on said company's website, they're already sufficiently up-front about it.

Thank you so much for that explanation!

Hey, lowly Bio undergrad here, but how are you able to skip the RNA extraction step? I read the paper and you use the viral transport medium, but wouldn't you have to also purify RNA from that (or is it just much easier to extract RNA from that medium)? I also dived into the paper behind the "skip the RNA extraction step" methodology and it basically seems to swap out one RNA extraction kit for another (Qiagen RNeasy Mini kit and the Qiagen RNeasy Micro kit). Couldn't shifting kits from one provider to another introduce supply chain strain? (or am I just oversimplifying it?)

This brings me back to orgo lab. A six step synthesis was torture when you were graded by your final yield exactly bc of the compounding nature of small mistakes in each step. Even if you get fairly good yields of 80 to 90% per step, you’d end up with a 53% at best and hope to god that everyone else did equally badly so the curve would save you.

It's semi significant because some companies are curtailing their internship programs right now.

Yes tons and tons. It's fairly easy to just imagine it as a sort of DFA/NFA. I personally like an SIR(D) model where from the "I" state you can either progress to recovered or dead. You can kinda take care of that in the regular "R" case with the SIR model, but the "D" state makes the fatality of the virus all the more visible. Additionally, you can add "Vital dynamics" to the model by basically adding a birth rate dependent on the total size of the reproducing population that feeds into the "S" state. You can also add a general baseline death rate (\mu) that takes away some population from each state in SIR. Lastly, like the sibling commenter mentioned, you can add an "E" state where there is a state between susceptible and infected where an individual is infected but not yet infectious. All that is added are more letters and computational time :)

Oh wow...google glass, that's something I haven't seen in a long time.

Yep! But there are a few more barriers in the way if your school does not offer AP courses (you'd need to find neighboring schools that do allow it and talk to the AP coordinator at that school). For subject tests, there are pretty minimal barriers associated with signing up. You just register as you normally would for an SAT exam IIRC.

Honestly welcome change. The SAT Subject tests (at least for people around me in HS) were always considered as a much easier test you'd take after the AP for that very same subject. I just equated it to another way for collegeboard to get money especially since the questions were way more straightforward than AP. I am slightly concerned about what this means for schools where AP classes are not offered (I imagine SAT Subject tests presented the most accessible opportunity for these students to demonstrate their aptitude in a subject).

EDIT: changed wording in response to child comment.

> If a function be advertised to return an error code in the event of difficulties, thou shalt check for that code, yea, even though the checks triple the size of thy code and produce aches in thy typing fingers, for if thou thinkest ``it cannot happen to me'', the gods shall surely punish thee for thy arrogance.

http://www.lysator.liu.se/c/ten-commandments.html

old.reddit.com still works!

Kinda both. Increased presence of amyloid (a type of protein) plaques are common in patients with Alzheimer's disease, so it was hypothesized that these plaques contribute to alzheimers. The drugs mentioned specifically targeted these plaques (via monoclonal antibodies). These drugs didn't slow the progression of alzheimers in the patient population that was certain to get it. The drugs could have failed for two reasons I think: 1) the antibodies from the drugs were unable to break up the plaques (in which case the drug is the issue) or 2) the amyloid plaques themselves are actually being broken up by the drug...but the plaques may not really be a cause of alzheimers (in which case the hypothesis is the issue).